TryOut1.Rmd

---
title: "121034"
output: html_notebook
---


```{r}
# sample exercise
## By Kogei
### November 2021
wbcd <- read.csv("wisc_bc_data.csv", stringsAsFactors = FALSE)
str(wbcd)
wbcd <- wbcd[-1] #to remove first column

table(wbcd$diagnosis)
wbcd$diagnosis <- factor(wbcd$diagnosis, levels = c("B", "M"),
                         labels = c("Benign", "Malignant"))
round(prop.table(table(wbcd$diagnosis)) * 100, digits = 1)
summary(wbcd[c("radius_mean", "area_mean", "smoothness_mean")])

#Normalizing numeric data
normalize <- function(x) {return ((x - min(x)) / (max(x) - min(x)))}
normalize(c(1, 2, 3, 4, 5))
normalize(c(10, 20, 30, 40, 50))

# lapply() takes a list and applies a function to each element of the list.
wbcd_n <- as.data.frame(lapply(wbcd[2:31], normalize))
summary(wbcd_n$area_mean)
wbcd_train <- wbcd_n[1:469, ]
wbcd_test <- wbcd_n[470:569, ]

wbcd_train_labels <- wbcd[1:469, 1]
wbcd_test_labels <- wbcd[470:569, 1]



library(class)
wbcd_test_pred <- knn(train = wbcd_train, test = wbcd_test,
                      cl = wbcd_train_labels, k=21)
library(gmodels)
CrossTable(x = wbcd_test_labels, y = wbcd_test_pred,
           prop.chisq=FALSE)


#improving model performance
wbcd_z <- as.data.frame(scale(wbcd[-1]))
summary(wbcd_z$area_mean)
wbcd_train <- wbcd_z[1:469, ]
wbcd_test <- wbcd_z[470:569, ]
wbcd_train_labels <- wbcd[1:469, 1]
wbcd_test_labels <- wbcd[470:569, 1]
wbcd_test_pred <- knn(train = wbcd_train, test = wbcd_test,
                        cl = wbcd_train_labels, k=21)
CrossTable(x = wbcd_test_labels, y = wbcd_test_pred,
             prop.chisq=FALSE)

```